Bitcion Price Change Graphic

import pandas as pd
import numpy as np
import plotly.graph_objects as go
from plotly.offline import plot
import matplotlib.pyplot as plt
import datetime
from pycoingecko import CoinGeckoAPI
from mplfinance.original_flavor import candlestick2_ohlc

#!pip install pycoingecko
#!pip install plotly
#!pip install mplfinance

cg = CoinGeckoAPI()

bitcoin_data = cg.get_coin_market_chart_by_id(id='bitcoin', vs_currency='usd', days=3600)

bitcoin_price_data = bitcoin_data['prices']

bitcoin_price_data[0:5]

[[1367107200000, 135.3],
 [1367193600000, 141.96],
 [1367280000000, 135.3],
 [1367366400000, 117.0],
 [1367452800000, 103.43]]

data = pd.DataFrame(bitcoin_price_data, columns=['TimeStamp', 'Price'])
data.shape

(3348, 2)

data['date'] = data['TimeStamp'].apply(lambda d: datetime.date.fromtimestamp(d/1000.0))

data

	TimeStamp	Price	date
0	1367107200000	135.300000	2013-04-27
1	1367193600000	141.960000	2013-04-28
2	1367280000000	135.300000	2013-04-29
3	1367366400000	117.000000	2013-04-30
4	1367452800000	103.430000	2013-05-01
...	...	...	...
3343	1656115200000	21262.713483	2022-06-24
3344	1656201600000	21525.880362	2022-06-25
3345	1656288000000	21052.805398	2022-06-26
3346	1656374400000	20751.477531	2022-06-27
3347	1656459545000	20272.787705	2022-06-28

3348 rows × 3 columns

candlestick_data = data.groupby(data.date, as_index=False).agg({"Price": ['min', 'max', 'first', 'last']})

candlestick_data.to_csv('Bitcion_Price.csv')

fig = go.Figure(data=[go.Candlestick(x=candlestick_data['date'],
                open=candlestick_data['Price']['first'], 
                high=candlestick_data['Price']['max'],
                low=candlestick_data['Price']['min'], 
                close=candlestick_data['Price']['last'])
                ])

fig.update_layout(xaxis_rangeslider_visible=False)

fig.show()